Sensor Fusion
Showing 11 of 11 projects
An interactive Jupyter Notebook book teaching Kalman and Bayesian filters through Python code and practical examples.
An open-source simulator built on Unreal Engine for developing, training, and validating autonomous driving systems.
An open-source project for developing autonomous vehicle software with datasets, models, and ROS components.
A real-time, tightly-coupled lidar-inertial odometry package for robust robot localization and mapping.
A computationally efficient and robust LiDAR-inertial odometry (LIO) package using a tightly-coupled iterated Kalman filter.
An optimization-based multi-sensor state estimator for accurate self-localization in drones, cars, and AR/VR applications.
A multi-sensor calibration toolbox for autonomous driving, supporting IMU, LiDAR, camera, and radar calibration.
An open research-oriented C++ framework for multi-session and multi-robot visual-inertial mapping and localization.
An efficient probabilistic 3D mapping framework based on octrees for robotics and computer vision applications.
A ROS package for real-time 6DOF SLAM using 3D LIDAR, featuring graph-based optimization with multiple sensor constraints.
A ROS package providing nonlinear state estimation nodes for robot localization using sensor fusion.
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